class_docstr: MobileNet-V3
  
  A this model utilizes the MobileNet-v3 specific 'efficient head', where global pooling is done before the
  head convolution without a final batch-norm layer before the classifier.
  
  Paper: https://arxiv.org/abs/1905.02244
code: def features(self, x):
      x = self.conv_stem(x)
      x = self.bn1(x)
      x = self.act1(x)
      x = self.blocks(x)
      x = self.global_pool(x)
      x = self.conv_head(x)
      x = self.act2(x)
      return x
  
imports: import torch.nn
  import torch.nn.functional
  from .activations import get_act_fn, get_act_layer, HardSwish
  from .config import layer_config_kwargs
  from .conv2d_layers import select_conv2d
  from .helpers import load_pretrained
  from .efficientnet_builder import *
label_desc: Use image node_id 3 for calling the MobileNetV3 obj's underlying member method code with example usage: obj.features(x) and returns: x